Monitoring the charging behavior of electric vehicle clusters will contribute to developing more effective energy management strategies for grid operators. A low implementation cost leads to a wide application prospect in nonintrusive monitoring for EVs. Aiming at the problem that traditional nonintrusive monitoring methods cannot identify unknown devices accurately due to the lack of classes, a nonintrusive monitoring method based on zero-shot learning (ZSL) is proposed in this article, one which can monitor the unknown types of EVs connected to charging piles. First, the charging characteristics of known EVs and unknown EVs are extracted by dictionary learning. Then EVs are classified by ZSL based on sparse coding. Furthermore, EVs are decomposed based on the proposed multimode factorial hidden Markov model (FHMM). Finally, the EV dataset of Pecan Street is used to verify the effectiveness and accuracy of the proposed method.
A Ternary Linear Compound T 2 and Its Phase Equilibrium Relationships inMg-Zn-Nd System at 400°C. -The title system is characterized by SEM, electron probe microanalysis, XRD, SAED, and TEM. A ternary compound with chemical formula (Mg,Zn)11.5Nd and a Zn content in the range from 26.9% to 41.3% exists in the Nd-poor corner of the title system. The crystal structure of this compound is C-centered orthorhombic. The (Mg,Zn) 3 phase has a range of Zn content from 43% to 49%, and a face-centered cubic crystal structure. -(HUANG, M. L.; LI*, H. X.; DING, H.; TANG, Z. Y.; MEI, R. B.; ZHOU, H. T.; REN, R. P.; HAO, S. M.; J. Alloys Compd.
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